def class_area_histogram_with_select(record_stats_list, class_map=None, width=500, height=500):
"""Creates histograms for areas from a list of record_stats."""
# gui
# remove the first entry from the class_map, because the background class is not explicit part of the annotaitons
unique_labels = sorted(pd.unique(record_stats_list[0]["label"]))
for record_stats in record_stats_list[1:]:
if not all(pd.unique(sorted(record_stats["label"])) == unique_labels):
raise ValueError("All dataframes in the records_stats_list need to have the same set of unique values.")
options = pd.unique(record_stats_list[0]["label"])
options.sort()
if class_map is not None:
options = np.vectorize(class_map.get_id)(options)
class_dropdown = pnw.Select(options=options.tolist())
bins_slider = pnw.IntSlider(name="Bins", start=10, end=100, step=5)
checkbox_normalized = pnw.Checkbox(name="Normalized", value=False)
@pn.depends(class_dropdown.param.value, bins_slider.param.value, checkbox_normalized.param.value)
def _draw_histogram(class_label, bins, normalized):
nonlocal class_map
nonlocal width
nonlocal height
nonlocal record_stats_list
class_label = class_label if class_map is None else class_map.get_name(class_label)
return pn.Row(*[draw_area_histogram(record_stats, class_label, class_map, bins, normalized, width, height) for record_stats in record_stats_list])
return pn.Column(class_dropdown, pn.Row(bins_slider, checkbox_normalized), _draw_histogram)
class PanelWithEbandsRobot(BaseRobotPanel): #, metaclass=abc.ABCMeta):
"""
Mixin class for panels with a robot that owns a list of of |ElectronBands|
"""
# Widgets to plot ebands.
ebands_plotter_mode = pnw.Select(
name="Plot Mode",
value="gridplot",
options=["gridplot", "combiplot", "boxplot",
"combiboxplot"]) # "animate",
ebands_plotter_btn = pnw.Button(name="Plot", button_type='primary')
ebands_df_checkbox = pnw.Checkbox(name='With Ebands DataFrame',
value=False)
# Widgets to plot edos.
edos_plotter_mode = pnw.Select(name="Plot Mode",
value="gridplot",
options=["gridplot", "combiplot"])
edos_plotter_btn = pnw.Button(name="Plot", button_type='primary')
def get_ebands_plotter_widgets(self):
return pn.Column(self.ebands_plotter_mode, self.ebands_df_checkbox,
self.ebands_plotter_btn)
@param.depends("ebands_plotter_btn.clicks")
def on_ebands_plotter_btn(self):
if self.ebands_plotter_btn.clicks == 0: return
ebands_plotter = self.robot.get_ebands_plotter()
plot_mode = self.ebands_plotter_mode.value
plotfunc = getattr(ebands_plotter, plot_mode, None)
if plotfunc is None:
raise ValueError("Don't know how to handle plot_mode: %s" %
plot_mode)
fig = plotfunc(**self.fig_kwargs)
col = pn.Column(self._mp(fig), sizing_mode='scale_width')
if self.ebands_df_checkbox.value:
df = ebands_plotter.get_ebands_frame(with_spglib=True)
col.append(self._df(df))
return pn.Row(col, sizing_mode='scale_width')
def get_edos_plotter_widgets(self):
return pn.Column(self.edos_plotter_mode, self.edos_plotter_btn)
@param.depends("edos_plotter_btn.clicks")
def on_edos_plotter_btn(self):
if self.edos_plotter_btn.clicks == 0: return
edos_plotter = self.robot.get_edos_plotter()
plot_mode = self.edos_plotter_mode.value
plotfunc = getattr(edos_plotter, plot_mode, None)
if plotfunc is None:
raise ValueError("Don't know how to handle plot_mode: %s" %
plot_mode)
fig = plotfunc(**self.fig_kwargs)
return pn.Row(pn.Column(self._mp(fig)), sizing_mode='scale_width')
def _generate_annotations_tab(self):
plot_size = min(floor(self.width / len(self.datasets)),
floor(self.height / 2))
link_plots_checkbox = pnw.Checkbox(name="Link plot axis", value=False)
@pn.depends(link_plots_checkbox.param.value)
def _mixing_plots(link_plots):
# mixing of classes
mixing_matrix_classes_in_images = [
utils.calculate_mixing_matrix(dataset,
self.IMAGE_IDENTIFIER_COL,
self.ANNOTATON_LABEL_COL)
for dataset in self._get_descriptor_for_all_datasets(
self.DESCRIPTOR_DATA)
]
class_mixing_matrix_plot = pn.Row(
"<b>Class mixing</b>",
*heatmap(mixing_matrix_classes_in_images,
"row_name",
"col_name",
"values",
link_plots=link_plots,
width=plot_size,
height=plot_size))
# number of object per image, stacked hist
classes_for_objects_per_image_stacked_hist = pn.Row(
"<b>Objects per Image</b>",
*stacked_hist(self._get_descriptor_for_all_datasets(
self.DESCRIPTOR_DATA),
self.OBJECTS_PER_IMAGE_COL,
self.ANNOTATON_LABEL_COL,
"Objects per Image",
link_plots=link_plots,
width=plot_size,
height=plot_size))
return pn.Column(link_plots_checkbox, class_mixing_matrix_plot,
classes_for_objects_per_image_stacked_hist)
# categorical overview
self.categorical_2d_histogram = categorical_2d_histogram_with_gui(
self._get_descriptor_for_all_datasets(self.DESCRIPTOR_DATA),
category_cols=["label", "num_annotations", "width", "height"],
hist_cols=[
"num_annotations", "area", "area_normalized",
"area_square_root", "area_square_root_normalized",
"bbox_ratio", "bbox_xmin", "bbox_xmax", "bbox_ymin",
"bbox_ymax", "bbox_width", "bbox_height", "width", "height"
],
height=floor(plot_size * 1.5),
width=floor(plot_size * 1.5))
return pn.Column(_mixing_plots,
self.categorical_2d_histogram,
align="center")
def __init__(
self,
df,
*,
feature_aliases=None,
describe=True,
x_label="timestamp",
port=5006,
websocket_origin=49179,
sample_rate=1.0,
plot_width=600,
plot_height=400,
**params,
):
BasePanel.__init__(
self,
df,
feature_aliases=feature_aliases,
port=port,
websocket_origin=websocket_origin,
sample_rate=sample_rate,
)
self.plot_width = plot_width
self.plot_height = plot_height
self.describe = describe
self.x_label = x_label
tags = sorted(list(df.columns))
tag = tags[0]
bounds = self.tag_bounds[tag]
# widget objects
self.feature = pnw.Select(value=tag, options=tags, name="Ticker")
self.description = pnw.Select(
value=self.feature_aliases[tag],
options=sorted(list(self.feature_aliases.values())),
name="Company",
)
self.tag_range = pnw.RangeSlider(start=bounds[0],
end=bounds[1],
value=bounds,
name="Feature Range")
self.log_scale = pnw.Checkbox(
name="Log scale",
value=False,
)
pm.Parameterized.__init__(self, **params)
def plot_all(data, alp=True, var='h_li'):
"""
Scatter plot with x=lon, y=lat.
Interactivity for missing data and color.
"""
# Static plot
def scatter(data=data, alp=alp, var=var):
clean = data[data.loc[:, var] < 1e+38].hvplot.scatter(x='longitude',
y='latitude',
c=var,
s=1,
alpha=0.2,
cmap='viridis',
hover=False)
missing = data[data.loc[:, var] > 1e+38].hvplot.scatter(
x='longitude',
y='latitude',
c='red',
s=1,
alpha=0.2 if alp == True else 0,
hover=False)
return (clean * missing)
# Widgets
var = pnw.Select(name='Color', value='h_li', options=list(data.columns))
alp = pnw.Checkbox(value=True, name='Missing data (red)')
text = "<br>\n# All ATL06 data for the selected area\nSelect a color variable and whether to show missing data"
# Interactive plot
@pn.depends(var, alp)
def reactive(var, alp):
return (scatter(data, alp, var))
# Layout
widgets = pn.Column(text, var, alp)
image = pn.Row(reactive, widgets)
return (image)
def categorical_2d_histogram_with_gui(data: pd.DataFrame, category_cols=None, hist_cols=None, width=500, height=500):
"""Creates a categorical_2d_histogram for a dataframe, where each option (except width, height and color_mapper) of the categorical_2d_histogram can be set with gui elements.
If the input is a list all dataframes need to have the same cols as the first dataframe in the list"""
if category_cols is None and isinstance(data, pd.DataFrame):
category_cols = data.columns.tolist()
elif category_cols is None and isinstance(data, list):
category_cols = data[0].columns.tolist()
if hist_cols is None and isinstance(data, pd.DataFrame):
hist_cols = [col_name for col_name in data.columns if is_numeric_dtype(data[col_name])]
elif hist_cols is None and isinstance(data, list):
hist_cols = [col_name for col_name in data[0].columns if is_numeric_dtype(data[0][col_name])]
x_select = pnw.Select(name="X-Axis", options=hist_cols)
y_select = pnw.Select(name="Y-Axis", options=category_cols)
axis_selector = pn.Row(x_select, y_select, width=width)
x_is_categorical = pnw.Checkbox(name="X is categorical", value=False)
x_bins = pnw.IntInput(name="Bins", start=1, end=500, value=10, disabled=False)
if isinstance(data, pd.DataFrame):
x_range_start=data[x_select.value].min()
x_range_end=data[x_select.value].max()
x_range_end=x_range_end if x_range_end > x_range_start else x_range_end*1.1
x_range_step=(x_range_end - x_range_start) / 50
elif isinstance(data, list):
x_range_start=min(df[x_select.value].min() for df in data)
x_range_end=max(df[x_select.value].max() for df in data)
x_range_end=x_range_end if x_range_end > x_range_start else x_range_end*1.1
x_range_step=(x_range_end - x_range_start) / 50
x_range = pnw.RangeSlider(name="X-Axis Range", start=x_range_start, end=x_range_end, step=x_range_step, disabled=False)
x_axis_configuration = pn.Row(x_range, x_bins, width=width)
normalize_rows = pnw.Checkbox(name="Normalize rows", value=False)
x_precision = pnw.IntInput(name="Precision", start=0, end=10, value=2, disabled=False)
additional_parameters = pn.Row(x_is_categorical, normalize_rows, x_precision, width=width)
config_gui = pn.Column(axis_selector, additional_parameters, x_axis_configuration, align="center")
@pn.depends(y_select.param.value, x_select.param.value, x_bins.param.value_throttled, x_range.param.value_throttled, x_is_categorical.param.value, normalize_rows.param.value, x_precision.param.value)
def _plot(category_col, hist_col, bins, range, x_is_categorical, normalize_rows, precision):
if x_is_categorical:
x_range.disabled = True
x_precision.disabled = True
x_bins.disabled = True
else:
x_range.disabled = False
x_precision.disabled = False
x_bins.disabled = False
if isinstance(data, pd.DataFrame):
if data[hist_col].min() != x_range.start or data[hist_col].max() != x_range.end:
x_range.start = data[hist_col].min()
x_range.end = data[hist_col].max() if data[hist_col].max() > x_range.start else data[hist_col].max()*1.1
x_range.value = (x_range.start, x_range.end)
x_range.step = (x_range.end-x_range.start)/50
elif isinstance(data, list):
if min(df[hist_col].min() for df in data) != x_range.start or max(df[hist_col].max() for df in data) != x_range.end:
x_range.start = min(df[hist_col].min() for df in data)
x_range.end = max(df[hist_col].max() for df in data) if max(df[hist_col].max() for df in data) > x_range.start else max(df[hist_col].max() for df in data)*1.1
x_range.value = (x_range.start, x_range.end)
x_range.step = (x_range.end-x_range.start)/50
range = x_range.value
if isinstance(data, list):
plot = pn.Row(*categorical_2d_histogram(data, category_col, hist_col, bins, range, normalize_rows, precision, color_mapper=None, hist_col_is_categorical=x_is_categorical, width=width, height=height))
elif isinstance(data, pd.DataFrame):
plot = pn.Row(categorical_2d_histogram(data, category_col, hist_col, bins, range, normalize_rows, precision, color_mapper=None, hist_col_is_categorical=x_is_categorical, width=width, height=height))
return plot
return pn.Column(config_gui, _plot)
def map_dash():
"""Map dashboard"""
# Create the map
map_pane = pn.pane.plot.Folium(sizing_mode="scale_both", min_width=800)
# Initialize map at Joshua Tree in July
unit = 'JOTR'
month = 7
coords = get_coords(unit, parks)
bnds = get_bounds(unit, boundaries)
traffic = get_traffic(unit, traffic_data)
visitors = get_visitors(unit, month, publicUse)
map_pane.object = makeMap(unit, month, coords, bnds, visitors, traffic)
# Create the dropdown menus for month and visitors
month_buttons = pnw.RadioButtonGroup(name='Month',
options=list(month_dict.keys()),
button_type='primary',
value='July')
traffic_checkbox = pnw.Checkbox(name='Display traffic counters',
value=True)
park_select = pnw.Select(name='Where do you want to go?',
options=list(park_dict.keys()),
value='Joshua Tree NP')
# Trigger map updates
def update_map(event):
month = month_dict[month_buttons.value]
unit = park_dict[park_select.value]
coords = get_coords(unit, parks)
bnds = get_bounds(unit, boundaries)
traffic = get_traffic(unit, traffic_data)
visitors = get_visitors(unit, month, publicUse)
map_pane.object = makeMap(unit,
month,
coords,
bnds,
visitors,
traffic,
useTraffic=traffic_checkbox.value)
return
# Updates
month_buttons.param.watch(update_map, 'value')
month_buttons.param.trigger('value')
park_select.param.watch(update_map, 'value')
park_select.param.trigger('value')
traffic_checkbox.param.watch(update_map, 'value')
traffic_checkbox.param.trigger('value')
# Fully return the map
app = pn.Column(month_buttons,
traffic_checkbox,
park_select,
map_pane,
width_policy='fit')
return app
#pn.extension()
#app = map_dash()
#app.servable()
#server = app.show(threaded=True)
def __init__(self, plot, tabs): # pylint: disable=redefined-outer-name
"""Initialize form.
:param plot: IsothermPlot instance for validation of results.
:param tabs: Panel tabs instance for triggering tab switching.
"""
self.plot = plot
self.tabs = tabs
# isotherm metadata
self.inp_doi = pw.TextInput(name='Article DOI',
placeholder='10.1021/jacs.9b01891')
self.inp_doi.param.watch(self.on_change_doi, 'value')
self.inp_temperature = pw.TextInput(name='Temperature',
placeholder='303')
self.inp_adsorbent = pw.AutocompleteInput(
name='Adsorbent Material',
options=QUANTITIES['adsorbents']['names'],
placeholder='Zeolite 5A',
case_sensitive=False,
**restrict_kwargs)
self.inp_isotherm_type = pw.Select(
name='Isotherm type',
options=['Select'] + QUANTITIES['isotherm_type']['names'])
self.inp_measurement_type = pw.Select(
name='Measurement type',
options=['Select'] + QUANTITIES['measurement_type']['names'])
self.inp_pressure_scale = pw.Checkbox(
name='Logarithmic pressure scale')
self.inp_isotherm_data = pw.TextAreaInput(
name='Isotherm Data',
height=200,
placeholder=config.SINGLE_COMPONENT_EXAMPLE)
self.inp_figure_image = pw.FileInput(name='Figure snapshot')
# units metadata
self.inp_pressure_units = pw.Select(
name='Pressure units',
options=['Select'] + QUANTITIES['pressure_units']['names'])
self.inp_pressure_units.param.watch(self.on_change_pressure_units,
'value')
self.inp_saturation_pressure = pw.TextInput(
name='Saturation pressure [bar]', disabled=True)
self.inp_adsorption_units = pw.AutocompleteInput(
name='Adsorption Units',
options=QUANTITIES['adsorption_units']['names'],
placeholder='mmol/g',
case_sensitive=False,
**restrict_kwargs)
# digitizer info
self.inp_source_type = pw.TextInput(name='Source description',
placeholder='Figure 1a')
self.inp_tabular = pw.Checkbox(
name='Tabular Data (i.e., not digitized from a graphical source)')
self.inp_digitizer = pw.TextInput(name='Digitized by',
placeholder='Your full name')
# fill form from JSON upload
self.inp_json = pw.FileInput(name='Upload JSON Isotherm')
# buttons
self.btn_prefill = pn.widgets.Button(
name='Prefill (default or from JSON)', button_type='primary')
self.btn_prefill.on_click(self.on_click_prefill)
self.out_info = bw.PreText(
text='Press "Plot" in order to download json.')
self.inp_adsorbates = Adsorbates(show_controls=False, )
self.btn_plot = pn.widgets.Button(name='Plot', button_type='primary')
self.btn_plot.on_click(self.on_click_plot)
for inp in self.required_inputs:
inp.css_classes = ['required']
# create layout
self.layout = pn.Column(
pn.pane.HTML('<h2>Isotherm Digitizer</h2>'),
self.inp_digitizer,
self.inp_doi,
pn.pane.HTML('<hr>'),
self.inp_source_type,
pn.Row(pn.pane.HTML("""Attach Figure Graphics"""),
self.inp_figure_image),
self.inp_measurement_type,
self.inp_adsorbent,
self.inp_adsorbates.column,
self.inp_temperature,
self.inp_isotherm_type,
pn.Row(self.inp_pressure_units, self.inp_saturation_pressure),
self.inp_pressure_scale,
self.inp_adsorption_units,
pn.pane.HTML("""We recommend the
<b><a href='https://apps.automeris.io/wpd/' target="_blank">WebPlotDigitizer</a></b>
for data extraction."""),
self.inp_isotherm_data,
self.inp_tabular,
pn.Row(self.btn_plot, self.btn_prefill, self.inp_json),
self.out_info,
)
import panel.widgets as pnw
import datetime
import pandas as pd
import us.states as uss
pn.extension()
pn.config.sizing_mode = "stretch_width"
stats_w = pnw.DataFrame(pd.DataFrame(), name='stats')
counties_w = pnw.DataFrame(pd.DataFrame(), name='counties')
sort_cols = [
'Confirmed', 'Deaths', 'Active', 'county', 'pop2019', 'fraction_confirmed',
'death_rate'
]
state_w = pnw.Select(name='state',
options=uss.states(),
value='Alabama',
disabled=False)
sortby_w = pnw.Select(name='sort on column',
options=sort_cols,
value='fraction_confirmed',
disabled=False)
ascending_w = pnw.Checkbox(name='Sort Ascending', value=False, disabled=False)
date_w = pnw.DatePicker(name='date',
value=datetime.date.today() - datetime.timedelta(1),
disabled=False)
def bam_viewer(bam_file, ref_file, gff_file=None, width=1000, height=200, color='gray'):
"""Bam viewer widget.
Args:
bam_file: sorted bam file
ref_file: reference sequence in fasta format
gff_file: optional genomic features file
"""
slider = pnw.IntSlider(name='location',start=1,end=10000,value=500,step=500,width=300)
main_pane = pn.pane.Bokeh(height=100)
cov_pane = pn.pane.Bokeh(height=60)
loc_pane = pn.pane.Str(50,width=250,style={'margin': '4pt'})
feat_pane = pn.pane.Bokeh(height=60)
ref_pane = pn.pane.Bokeh(height=60)
xzoom_slider = pnw.IntSlider(name='x zoom',start=50,end=8000,value=1000,step=10,width=100)
yzoom_slider = pnw.IntSlider(name='y zoom',start=10,end=100,value=20,step=5,width=100)#,orientation='vertical')
panleft_btn = pnw.Button(name='<',width=50,button_type='primary')
panright_btn = pnw.Button(name='>',width=50,button_type='primary')
chroms_select = pnw.Select(name='Chromosome', options=[], width=250)
colorby = pnw.Select(name='Color by', options=['quality','pair orientation','read strand'], width=180)
search_pane = pnw.TextInput(name='search',width=200)
trans_option = pnw.Checkbox(name='show translation')
debug_pane = pn.pane.Markdown()
def pan_right(event):
plot = main_pane.object
plot.x_range
start = slider.value
loc = slider.value+100
slider.value=loc
update(event)
return
def pan_left(event):
loc = slider.value-100
if loc<1:
return
slider.value=loc
update(event)
return
def update_features():
"""Load features"""
if gff_file is None:
return
ext = os.path.splitext(gff_file)[1]
if ext in ['.gff','.gff3']:
feats = utils.gff_to_features(gff_file)
elif ext in ['.gb','.gbff']:
feats = utils.genbank_to_features(gff_file)
p = feat_pane.object = plotters.plot_features(feats,
plot_width=width, plot_height=100)
return p
def search_features(event):
"""Find a feature"""
term = search_pane.value
feats = utils.gff_to_features(gff_file)
df = utils.features_to_dataframe(feats)
df['gene'] = df.gene.fillna('')
f = df[df.gene.str.contains(term)].iloc[0]
debug_pane.object = str(f.start)
slider.value = int(f.start)
update(event)
return
def update_ref(filename, start, end):
"""Update reference sequence"""
if filename == None:
return
seqlen = utils.get_fasta_length(filename)
slider.end = seqlen
refseq = Fasta(filename)
chroms = list(refseq.keys())
chroms_select.options = chroms
key = chroms[0]
seq = refseq[key][int(start):int(end)].seq
ref_pane.object = plotters.plot_sequence(seq, plot_height=50,fontsize='9pt',xaxis=False)
return
def update(event):
"""Update viewers on widget change"""
xzoom = xzoom_slider.value
yzoom = yzoom_slider.value
start = slider.value
N = xzoom/2
end = start+N
chrom = utils.get_chrom(bam_file)
loc_pane.object = '%s:%s-%s' %(chrom,start,int(end))
cov = utils.get_coverage(bam_file,chrom,start,end)
cov_pane.object = plotters.plot_coverage(cov,plot_width=width)
main_pane.object = plotters.plot_bam_alignment(bam_file,chrom,start,end,height=yzoom,plot_width=width,plot_height=height)
update_ref(ref_file, start, end)
if feature_plot:
feature_plot.x_range.start = start
feature_plot.x_range.end = end
debug_pane.object = ''
return
slider.param.watch(update, 'value')
xzoom_slider.param.watch(update, 'value')
yzoom_slider.param.watch(update, 'value')
panright_btn.param.watch(pan_right, 'clicks')
panleft_btn.param.watch(pan_left, 'clicks')
search_pane.param.watch(search_features, 'value')
feature_plot = update_features()
#initialise slider
slider.param.trigger('value')
#menus = pn.Row(bam_input, ref_input, gff_input)
top = pn.Row(slider,xzoom_slider,yzoom_slider,panleft_btn,panright_btn,loc_pane)
bottom = pn.Row(chroms_select, search_pane,colorby,trans_option)
app = pn.Column(top,cov_pane,main_pane,ref_pane,feat_pane,bottom,debug_pane)
return app
def _update_widgets_panel(self):
self._default_component[self.component_type] = self.component
component = None
controls = None
if self.component is pn.pane.HoloViews:
component = pn.pane.HoloViews(_create_hvplot())
if self.component is pn.pane.ECharts:
# Issue https://github.com/holoviz/panel/issues/1817
component = pn.pane.ECharts(_create_echarts_plot(),
min_height=400,
min_width=200,
sizing_mode="stretch_both")
if self.component is pnw.Ace:
py_code = inspect.getsource(_create_hvplot)
component = pnw.Ace(
value=py_code,
sizing_mode="stretch_width",
language="python",
height=400,
theme=self._ace_theme,
)
elif self.component is pnw.AutocompleteInput:
component = pnw.AutocompleteInput(
name="Autocomplete Input",
options=["Biology", "Chemistry", "Physics"],
placeholder="Write something here",
)
elif self.component is pnw.Button:
component = pnw.Button(name="Click me", button_type="primary")
elif self.component is pnw.CheckBoxGroup:
component = pnw.CheckBoxGroup(
name="Checkbox Group",
value=["Apple", "Pear"],
options=["Apple", "Banana", "Pear", "Strawberry"],
inline=True,
)
elif self.component is pnw.CheckButtonGroup:
component = pnw.CheckButtonGroup(
name="Check Button Group",
value=["Apple", "Pear"],
options=["Apple", "Banana", "Pear", "Strawberry"],
button_type="success",
)
elif self.component is pnw.Checkbox:
component = pnw.Checkbox(name="Checkbox")
elif self.component is pnw.ColorPicker:
component = pnw.ColorPicker(name="Color Picker", value="#DF3874")
elif self.component is pnw.CrossSelector:
component = pnw.CrossSelector(
name="Fruits",
value=["Apple", "Pear"],
options=["Apple", "Banana", "Pear", "Strawberry"],
height=300,
)
elif self.component is pnw.DataFrame:
component = self.component(name="Hello")
component.value = get_dataframe()
component.formatters = get_default_formatters(component.value)
controls = pn.Spacer()
elif self.component is pnw.DatePicker:
component = pnw.DatePicker(name="Date Picker")
# Issue: https://github.com/holoviz/panel/issues/1810
# component.start = date(2020, 1, 20)
# component.end = date(2020, 2, 20)
# component.value = date(2020, 2, 18)
elif self.component is pnw.DateRangeSlider:
component = self.component(name="Hello")
component.start = date(2020, 1, 20)
component.end = date(2020, 2, 20)
component.value = (date(2020, 2, 18), date(2020, 2, 20))
elif self.component is pnw.DateSlider:
component = self.component(name="Hello")
component.start = date(2020, 1, 20)
component.end = date(2020, 2, 20)
component.value = date(2020, 2, 18)
elif self.component is pnw.DatetimeInput:
component = self.component(name="Hello")
component.value = datetime(2020, 2, 18, 1, 2, 3)
elif self.component is pnw.DatetimeRangeInput:
component = self.component(
name="Hello",
start=datetime(2020, 1, 20),
end=datetime(2020, 2, 20),
value=(datetime(2020, 2, 18), datetime(2020, 2, 20)),
)
elif self.component is pnw.DiscretePlayer:
component = pnw.DiscretePlayer(
name="Discrete Player",
options=[2, 4, 8, 16, 32, 64, 128],
value=32,
loop_policy="loop",
)
elif self.component is pnw.DiscreteSlider:
component = pnw.DiscreteSlider(name="Discrete Slider",
options=[2, 4, 8, 16, 32, 64, 128],
value=32)
elif self.component is pnw.FileDownload:
component = pnw.FileDownload(file="README.md",
filename="README.md")
elif self.component is pnw.FileInput:
component = pnw.FileInput(accept=".csv,.json")
elif self.component is pnw.FileSelector:
component = pnw.FileSelector(name="Hello", max_height=400)
elif self.component is pnw.FloatInput:
component = pnw.FloatInput(name="FloatInput",
value=5.0,
step=1e-1,
start=0,
end=1000)
elif self.component is pnw.FloatSlider:
component = pnw.FloatSlider(name="Float Slider",
start=0,
end=3.141,
step=0.01,
value=1.57)
elif self.component is pnw.IntInput:
component = pnw.IntInput(name="IntInput",
value=5,
step=2,
start=0,
end=1000)
elif self.component is pnw.IntRangeSlider:
component = pnw.IntRangeSlider(name="Integer Range Slider",
start=0,
end=100,
value=(8, 40),
step=2)
elif self.component is pnw.IntSlider:
component = pnw.IntSlider(name="Integer Slider",
start=0,
end=20,
step=2,
value=4)
elif self.component is pnw.LiteralInput:
component = pnw.LiteralInput(name="Literal Input (dict)",
value={"key": [1, 2, 3]},
type=dict)
elif self.component is pnw.MenuButton:
menu_items = [
("Option A", "a"),
("Option B", "b"),
("Option C", "c"),
None,
("Help", "help"),
]
component = pnw.MenuButton(name="Dropdown",
items=menu_items,
button_type="primary")
elif self.component is pnw.MultiChoice:
component = pnw.MultiChoice(
name="MultiSelect",
value=["Apple", "Pear"],
options=["Apple", "Banana", "Pear", "Strawberry"],
)
elif self.component is pnw.MultiSelect:
component = pnw.MultiSelect(
name="MultiSelect",
value=["Apple", "Pear"],
options=["Apple", "Banana", "Pear", "Strawberry"],
size=8,
)
elif self.component is pnw.PasswordInput:
component = pnw.PasswordInput(name="Password Input",
placeholder="Enter a string here...")
elif self.component is pnw.Player:
component = pnw.Player(name="Player",
start=0,
end=100,
value=32,
loop_policy="loop")
elif self.component is pnw.Progress:
component = pnw.Progress(name="Progress", value=20, width=200)
elif self.component is pnw.RadioBoxGroup:
component = pnw.RadioBoxGroup(
name="RadioBoxGroup",
options=["Biology", "Chemistry", "Physics"],
inline=True)
elif self.component is pnw.RadioButtonGroup:
component = pnw.RadioButtonGroup(
name="Radio Button Group",
options=["Biology", "Chemistry", "Physics"],
button_type="success",
)
elif self.component is pnw.RangeSlider:
component = pnw.RangeSlider(
name="Range Slider",
start=0,
end=math.pi,
value=(math.pi / 4.0, math.pi / 2.0),
step=0.01,
)
elif self.component is pnw.Select:
component = pnw.Select(name="Select",
options=["Biology", "Chemistry", "Physics"])
elif self.component is pnw.StaticText:
component = pnw.StaticText(name="Static Text", value="A string")
elif self.component is pnw.TextAreaInput:
component = pnw.input.TextAreaInput(
name="Text Area Input", placeholder="Enter a string here...")
elif self.component is pnw.TextInput:
component = pnw.TextInput(name="Text Input",
placeholder="Enter a string here...")
elif self.component == pnw.Toggle:
component = pnw.Toggle(name="Toggle", button_type="success")
elif self.component == pnw.VideoStream:
component = pnw.VideoStream(name="Video Stream",
sizing_mode="stretch_width",
height=300)
if not component:
component = self.component(name="Hello")
if not controls:
controls = component.controls()
controls.margin = 0
self._component_panel[:] = [
pn.pane.Markdown("## " + component.__class__.name + " " +
self.component_type),
component,
pn.layout.Divider(),
pn.pane.Markdown("## Parameters"),
controls,
]
class FlowPanel(AbipyParameterized):
"""
"""
verbose = pn.widgets.IntSlider(start=0, end=10, step=1, value=0)
engine = pn.widgets.Select(value="fdp",
options=[
'dot', 'neato', 'twopi', 'circo', 'fdp',
'sfdp', 'patchwork', 'osage'
])
dirtree = pn.widgets.Checkbox(name='Dirtree', value=False)
graphviz_btn = pn.widgets.Button(name="Show graph", button_type='primary')
status_btn = pn.widgets.Button(name="Show status", button_type='primary')
history_btn = pn.widgets.Button(name="Show history", button_type='primary')
debug_btn = pn.widgets.Button(name="Debug", button_type='primary')
events_btn = pn.widgets.Button(name="Events", button_type='primary')
corrections_btn = pn.widgets.Button(name="Corrections",
button_type='primary')
handlers_btn = pn.widgets.Button(name="Handlers", button_type='primary')
vars_text = pn.widgets.TextInput(
name='Abivars',
placeholder='Enter list of variables separated by comma')
vars_btn = pn.widgets.Button(name="Show Variables", button_type='primary')
dims_btn = pn.widgets.Button(name="Show Dimensions", button_type='primary')
structures_btn = pn.widgets.Button(name="Show Structures",
button_type='primary')
structures_io_checkbox = pn.widgets.CheckBoxGroup(
name='Input/Output Structure',
value=['output'],
options=['input', 'output'],
inline=True)
# Widgets to plot ebands.
ebands_btn = pn.widgets.Button(name="Show Ebands", button_type='primary')
ebands_plotter_mode = pnw.Select(
name="Plot Mode",
value="gridplot",
options=["gridplot", "combiplot", "boxplot",
"combiboxplot"]) # "animate",
ebands_plotter_btn = pnw.Button(name="Plot", button_type='primary')
ebands_df_checkbox = pnw.Checkbox(name='With Ebands DataFrame',
value=False)
ebands_ksamp_checkbox = pn.widgets.CheckBoxGroup(
name='Input/Output Structure',
value=["with_path", "with_ibz"],
options=['with_path', 'with_ibz'],
inline=True)
#TODO: Implement widget for selected_nids(flow, options),
#radio_group = pn.widgets.RadioButtonGroup(
# name='Radio Button Group', options=['Biology', 'Chemistry', 'Physics'], button_type='success')
def __init__(self, flow, **params):
super().__init__(**params)
self.flow = flow
@param.depends('status_btn.clicks')
def on_status_btn(self):
if self.status_btn.clicks == 0: return
stream = StringIO()
self.flow.show_status(stream=stream, verbose=self.verbose.value)
return pn.Row(bkw.PreText(text=stream.getvalue()))
@param.depends('history_btn.clicks')
def on_history_btn(self):
if self.history_btn.clicks == 0: return
stream = StringIO()
#flow.show_history(status=options.task_status, nids=selected_nids(flow, options),
# full_history=options.full_history, metadata=options.metadata)
self.flow.show_history(stream=stream)
return pn.Row(bkw.PreText(text=stream.getvalue()))
@param.depends('graphviz_btn.clicks')
def on_graphviz_btn(self):
"""
"""
if self.graphviz_btn.clicks == 0: return
node = self.flow
if self.dirtree.value:
graph = node.get_graphviz_dirtree(engine=self.engine.value)
else:
graph = node.get_graphviz(engine=self.engine.value)
return pn.Column(graph)
@param.depends('debug_btn.clicks')
def on_debug_btn(self):
if self.debug_btn.clicks == 0: return
#TODO https://github.com/ralphbean/ansi2html ?
stream = StringIO()
#flow.debug(status=options.task_status, nids=selected_nids(flow, options))
self.flow.debug(stream=stream)
return pn.Row(bkw.PreText(text=stream.getvalue()))
@param.depends('events_btn.clicks')
def on_events_btn(self):
if self.events_btn.clicks == 0: return
stream = StringIO()
self.flow.show_events(stream=stream)
#flow.show_events(status=options.task_status, nids=selected_nids(flow, options))
return pn.Row(bkw.PreText(text=stream.getvalue()))
@param.depends('corrections_btn.clicks')
def on_corrections_btn(self):
if self.corrections_btn.clicks == 0: return
stream = StringIO()
self.flow.show_corrections(stream=stream)
#flow.show_corrections(status=options.task_status, nids=selected_nids(flow, options))
return pn.Row(bkw.PreText(text=stream.getvalue()))
@param.depends('handlers_btn.clicks')
def on_handlers_btn(self):
#if self.handlers_btn.clicks == 0: return
stream = StringIO()
#if options.doc:
# flowtk.autodoc_event_handlers()
#else:
#show_events(self, status=None, nids=None, stream=sys.stdout):
self.flow.show_event_handlers(verbose=self.verbose.value,
stream=stream)
return pn.Row(bkw.PreText(text=stream.getvalue()))
@param.depends('vars_btn.clicks')
def on_vars_btn(self):
if self.vars_btn.clicks == 0: return
if not self.vars_text.value: return
varnames = [s.strip() for s in self.vars_text.value.split(",")]
df = self.flow.compare_abivars(
varnames=varnames, # nids=selected_nids(flow, options),
printout=False,
with_colors=False)
return pn.Row(self._df(df))
@param.depends('dims_btn.clicks')
def on_dims_btn(self):
if self.dims_btn.clicks == 0: return
df = self.flow.get_dims_dataframe( # nids=selected_nids(flow, options),
printout=False,
with_colors=False)
return pn.Row(self._df(df), sizing_mode="scale_width")
@param.depends('structures_btn.clicks')
def on_structures_btn(self):
if self.structures_btn.clicks == 0: return
what = ""
if "input" in self.structures_io_checkbox.value: what += "i"
if "output" in self.structures_io_checkbox.value: what += "o"
dfs = self.flow.compare_structures(
nids=None, # select_nids(flow, options),
what=what,
verbose=self.verbose.value,
with_spglib=False,
printout=False,
with_colors=False)
return pn.Row(self._df(dfs.lattice), sizing_mode="scale_width")
@param.depends('ebands_plotter_btn.clicks')
def on_ebands_btn(self):
if self.ebands_plotter_btn.clicks == 0: return
df, ebands_plotter = self.flow.compare_ebands(
nids=None, # select_nids(flow, options),
with_path="with_path" in self.ebands_ksamp_checkbox.value,
with_ibz="with_ibz" in self.ebands_ksamp_checkbox.value,
verbose=self.verbose.value,
with_spglib=False)
if ebands_plotter is None:
return
plot_mode = self.ebands_plotter_mode.value
plotfunc = getattr(ebands_plotter, plot_mode, None)
if plotfunc is None:
raise ValueError("Don't know how to handle plot_mode: %s" %
plot_mode)
fig = plotfunc(**self.fig_kwargs)
col = pn.Column(self._mp(fig))
if self.ebands_df_checkbox.value:
col.append(self._df(df))
return pn.Row(col) #, sizing_mode='scale_width')
def get_panel(self):
"""Return tabs with widgets to interact with the flow."""
tabs = pn.Tabs()
app = tabs.append
#row = pn.Row(bkw.PreText(text=self.ddb.to_string(verbose=self.verbose.value), sizing_mode="scale_both"))
app(("Status", pn.Row(self.status_btn, self.on_status_btn)))
app(("History", pn.Row(self.history_btn, self.on_history_btn)))
app(("Events", pn.Row(self.events_btn, self.on_events_btn)))
app(("Corrections",
pn.Row(self.corrections_btn, self.on_corrections_btn)))
app(("Handlers", pn.Row(self.handlers_btn, self.on_handlers_btn)))
app(("Structures",
pn.Row(
pn.Column(self.structures_io_checkbox, self.structures_btn),
self.on_structures_btn)))
ws = pn.Column(self.ebands_plotter_mode, self.ebands_ksamp_checkbox,
self.ebands_df_checkbox, self.ebands_plotter_btn)
app(("Ebands", pn.Row(ws, self.on_ebands_btn)))
app(("Abivars",
pn.Row(pn.Column(self.vars_text, self.vars_btn),
self.on_vars_btn)))
app(("Dims", pn.Row(pn.Column(self.dims_btn), self.on_dims_btn)))
app(("Debug", pn.Row(self.debug_btn, self.on_debug_btn)))
app(("Graphviz",
pn.Row(pn.Column(self.engine, self.dirtree, self.graphviz_btn),
self.on_graphviz_btn)))
return tabs
def predictions_dashboard(path):
"""Dashboard for viewing results from epitopepredict runs."""
#folder_input = pn.widgets.TextInput(name='path', value='../zaire_test', width=400,width_policy='fit')
#reload_btn = pn.widgets.Button(name='reload',width=100,button_type='primary')
names = web.get_file_lists(path)
if names is None:
return
preds = web.get_predictors(path, name=names[0])
print(preds)
seqname = pnw.Select(name='name', value=names[0], options=names)
cutoff_slider = pnw.FloatSlider(name='cutoff',
value=.95,
start=.75,
end=.99,
step=0.01)
cutoff_method = pnw.Select(name='cutoff method',
value='default',
options=['default', 'rank'])
n_select = pnw.FloatSlider(name='n', value=1, start=1, end=8, step=1)
plot_select = pnw.Select(name='plot view',
value='tracks',
options=['tracks', 'sequence'])
table_select = pnw.Select(name='table view',
value='promiscuous',
options=['promiscuous', 'binders'])
colorseq_box = pnw.Checkbox(name='color sequences', value=False)
header = pn.pane.Markdown('__total sequences: %s__' % len(names),
css_classes=['main'])
tables = pn.Tabs(width=900)
plot = pn.pane.Bokeh(width=800)
debug = pn.pane.Markdown('test',
style={
'font-size': '10pt',
'background-color': 'yellow'
})
summary_plot = pn.pane.Bokeh()
summary_table_tabs = pn.Tabs()
recalc_button = pnw.Button(name='recalculate', width=200)
def update_banner():
"""Update the banner"""
fullpath = os.path.abspath(path)
banner = pn.Row(
pn.pane.Markdown(
'<h4>epitopepredict: %s</h4> [help](%s) version %s' %
(fullpath, helppage, __version__),
css_classes=['divheader'],
sizing_mode='stretch_width'))
return banner
def update_header(target, event):
names = web.get_file_lists(event.new)
target.object = "_total sequences: %s_" % str(len(names))
return
def callback_getpath(event):
path = os.path.getcwd()
folder.value = path
def update_plot(preds, name, cutoff, n, kind):
"""Plot data view"""
if kind == 'tracks':
p = plotting.bokeh_plot_tracks(preds,
name=name,
cutoff=cutoff,
n=n,
width=1000,
title=name)
plot.object = p
elif kind == 'sequence':
p = plotting.bokeh_plot_sequence(preds,
name=name,
cutoff=cutoff,
n=n,
width=1000,
title=name,
color_sequence=colorseq_box.value)
plot.object = p
return p
def update_tables(preds, name, n):
"""Tabular views of results"""
P = preds[0]
view = table_select.value
tables.clear()
for P in preds:
if view == 'promiscuous':
df = P.promiscuous_binders(n=n, name=name)
else:
df = P.get_binders(name=name)
res = df.to_html(classes="tinytable sortable")
div = '<div class="scrollingArea">%s</div>' % res
tables.append((P.name, div))
#tables.append((P.name,pn.pane.HTML('<p>hddsadsadsasda</p>',width=700)))
return
def update(event):
"""Update all elements"""
name = seqname.value
n = n_select.value
cutoff = cutoff_slider.value
kind = plot_select.value
debug.object = name
preds = web.get_predictors(path, name=name)
update_plot(preds, name=name, cutoff=cutoff, n=n, kind=kind)
update_tables(preds, name, n)
return
def update_summary(path):
"""Summary info for folder"""
data = web.get_summary_tables(path)
df = pd.concat(data, sort=True).reset_index()
#plot = plotting.bokeh_summary_plot(df)
#summary_plot.object = plot
summary_table_tabs.clear()
a = web.aggregate_summary(data)
div = web.get_scrollable_table(a)
summary_table_tabs.append(('all', div))
names = list(data.keys())
for n in names:
df = data[n]
res = df.to_html(classes="tinytable sortable")
div = '<div class="scrollingArea">%s</div>' % res
summary_table_tabs.append((n, div))
return
@pn.depends(seqname.param.value, n_select.param.value)
def download_link(name, n):
if preds is None:
return
df = preds[0].promiscuous_binders(n=n, name=name)
df.to_csv()
return pn.Pane(HTML('<a>download</a>'), width=700)
info = pn.pane.Markdown(web.get_readme())
banner = update_banner()
update_summary(path)
#reload_btn.param.watch(load_predictors, 'clicks')
#reload_btn.param.trigger()
seqname.param.watch(update, 'value')
cutoff_slider.param.watch(update, 'value')
n_select.param.watch(update, 'value')
table_select.param.watch(update, 'value')
plot_select.param.watch(update, 'value')
seqname.param.trigger('options', 'value')
top = pn.Row(header) #,download_link)
left = pn.Column(plot, tables, margin=10, sizing_mode='stretch_width')
right = pn.Column(seqname,
cutoff_slider,
cutoff_method,
n_select,
plot_select,
table_select,
colorseq_box,
css_classes=['widget-box'],
width=200)
center = pn.Row(left, right)
#bottom = pn.Row(table)
main = pn.Column(top, center)
summarypane = pn.Column(recalc_button, (pn.Row(summary_table_tabs)))
tabs = pn.Tabs(('summary', summarypane), ('sequence', main),
('about', info))
#tabs.append()
app = pn.Column(banner, tabs, sizing_mode='stretch_width')
return app
class PanelWithElectronBands(AbipyParameterized): #, metaclass=abc.ABCMeta):
# Bands plot
with_gaps = pnw.Checkbox(name='Show gaps')
#ebands_ylims
#ebands_e0
# e0: Option used to define the zero of energy in the band structure plot. Possible values:
# - `fermie`: shift all eigenvalues to have zero energy at the Fermi energy (`self.fermie`).
# - Number e.g e0=0.5: shift all eigenvalues to have zero energy at 0.5 eV
# - None: Don't shift energies, equivalent to e0=0
set_fermie_to_vbm = pnw.Checkbox(name="Set Fermie to VBM")
plot_ebands_btn = pnw.Button(name="Plot e-bands", button_type='primary')
# DOS plot.
edos_method = pnw.Select(name="e-DOS method",
options=["gaussian", "tetra"])
edos_step = pnw.Spinner(name='e-DOS step (eV)',
value=0.1,
step=0.05,
start=1e-6,
end=None)
edos_width = pnw.Spinner(name='e-DOS Gaussian broadening (eV)',
value=0.2,
step=0.05,
start=1e-6,
end=None)
plot_edos_btn = pnw.Button(name="Plot e-DOS", button_type='primary')
# Fermi surface plot.
fs_viewer = pnw.Select(name="FS viewer",
options=["matplotlib", "xcrysden"])
plot_fermi_surface_btn = pnw.Button(name="Plot Fermi surface",
button_type='primary')
#@abc.abstractproperty
#def ebands(self):
# """Returns the |ElectronBands| object."""
def get_plot_ebands_widgets(self):
"""Widgets to plot ebands."""
return pn.Column(self.with_gaps, self.set_fermie_to_vbm,
self.plot_ebands_btn)
@param.depends('plot_ebands_btn.clicks')
def on_plot_ebands_btn(self):
"""Button triggering ebands plot."""
if self.plot_ebands_btn.clicks == 0: return
if self.set_fermie_to_vbm.value:
self.ebands.set_fermie_to_vbm()
fig1 = self.ebands.plot(e0="fermie",
ylims=None,
with_gaps=self.with_gaps.value,
max_phfreq=None,
fontsize=8,
**self.fig_kwargs)
fig2 = self.ebands.kpoints.plot(**self.fig_kwargs)
row = pn.Row(self._mp(fig1),
self._mp(fig2)) #, sizing_mode='scale_width')
text = bkw.PreText(text=self.ebands.to_string(verbose=self.verbose))
return pn.Column(row, text, sizing_mode='scale_width')
def get_plot_edos_widgets(self):
"""Widgets to compute e-DOS."""
return pn.Column(self.edos_method, self.edos_step, self.edos_width,
self.plot_edos_btn)
@param.depends('plot_edos_btn.clicks')
def on_plot_edos_btn(self):
"""Button triggering edos plot."""
if self.plot_edos_btn.clicks == 0: return
edos = self.ebands.get_edos(method=self.edos_method.value,
step=self.edos_step.value,
width=self.edos_width.value)
fig = edos.plot(**self.fig_kwargs)
return pn.Row(self._mp(fig), sizing_mode='scale_width')
def get_plot_fermi_surface_widgets(self):
"""Widgets to compute e-DOS."""
return pn.Column(self.fs_viewer, self.plot_fermi_surface_btn)
@param.depends('plot_fermi_surface_btn.clicks')
def on_plot_fermi_surface_btn(self):
if self.plot_fermi_surface_btn.clicks == 0: return
# Cache eb3d
if hasattr(self, "_eb3d"):
eb3d = self._eb3d
else:
# Build ebands in full BZ.
eb3d = self._eb3d = self.ebands.get_ebands3d()
if self.fs_viewer.value == "matplotlib":
# Use matplotlib to plot isosurfaces corresponding to the Fermi level (default)
# Warning: requires skimage package, rendering could be slow.
fig = eb3d.plot_isosurfaces(e0="fermie",
cmap=None,
**self.fig_kwargs)
return pn.Row(self._mp(fig), sizing_mode='scale_width')
else:
raise ValueError("Invalid choice: %s" % self.fs_viewer.value)